Evidence for Substrate Influence on Artificial Substrate Invertebrate Communities
Bibliographic record
Abstract
Cobble baskets are frequently used as a tool to measure differences in benthic macroinvertebrate communities between waterbodies; however, underlying differences in substrate type may influence the resultant colonization of baskets, misrepresenting communities. This study tests the hypothesis that cobble basket placement influences the resulting benthic macroinvertebrate community. Cobble basket arrays (n = 4) were deployed in Dog Lake, Saskatchewan, in 2011 (97 d) and 2012 (95 d) on cobble habitats and soft or sandy substrates ∼100 m apart. Baskets placed on cobble substrate had significantly higher Shannon-Weaver diversity relative to those placed on soft substrate in both years, and higher % EPT (Ephemeroptera Plecoptera Trichoptera) in 2011, but total density was not significantly different. Nonmetric multidimensional scaling revealed that the community was different between both treatments, characterized by higher densities of Gammarus lacustris Sars in baskets placed on soft sediment in both years, higher densities of Aeshna sp. and Mystacides sp. on cobble substrate in 2011, and higher densities of Helobdella stagnalis (L.) and Glossophinia complanata (L.) on cobble substrate in 2012. The results were consistent with the hypothesis that baskets placed on cobble substrate versus soft substrate will result in differing community colonization. The resulting recommendation for monitoring and assessment using cobble baskets in lakes is that baskets be placed on comparable substrate type when comparing between lakes, and that cobble beds be chosen as a more appropriate substrate for deployment, as the added habitat complexity of baskets on soft sediment may act as an attractant and not reflect the true community composition of that habitat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".